suppressPackageStartupMessages({
library(Seurat)
library(org.Dr.eg.db)
library(BSgenome.Drerio.UCSC.danRer11)
library(Signac)
library(knitr)
library(kableExtra)
library(dplyr)
library(ggplot2)
library(ggsci)
library(limma)
library(JASPAR2020)
library(patchwork)
library(TFBSTools)
library(motifmatchr)
library(AnnotationHub)
library(harmony)
})
options(future.globals.maxSize = 4000 * 1024^2)
mypal <- pal_igv(palette = "default",alpha = 1)(30)
2. Read data
HB13hpf <- readRDS(file = "RDSfiles/HB13hpf_neural.RDS")
DefaultAssay(HB13hpf) <- "SCT"
Idents(HB13hpf) <- "Clusters"
HB16hpf <- readRDS(file = "RDSfiles/HB16hpf_neural.RDS")
DefaultAssay(HB16hpf) <- "SCT"
Idents(HB16hpf) <- "Clusters"
HB.int <- readRDS(file = "RDSfiles/int.neural.3WT.subset.RDS")
DefaultAssay(HB.int) <- "SCT"
DimPlot(HB.int, reduction = "wnn.umap") + scale_color_igv()

HB.int$intClusters <- as.character(HB.int$intClusters)
HB.int$intClusters[HB.int$intClusters %in% c("r1&r2.1","r1&r2.2")] <- "r1&r2"
HB.int$intClusters[HB.int$intClusters %in% c("r3.1")] <- "r3"
HB.int$intClusters[HB.int$intClusters %in% c("r4.1","r4.2")] <- "r4"
HB.int$intClusters[HB.int$intClusters %in% c("r5.1","r5.2")] <- "r5"
HB.int$intClusters[HB.int$intClusters %in% c("r6.1","r6.2")] <- "r6"
HB.int$intClusters <- droplevels(as.factor(HB.int$intClusters))
Idents(HB.int) <- "intClusters"
levels(HB.int)
[1] "CaudHB.1" "CaudHB.2" "CaudHB.3" "CaudHB.4" "Ciliated" "HB" "MB.1"
[8] "MB.2" "MB.3" "MHB.1" "MHB.2" "MHB.3" "MHB.4" "MHB.5"
[15] "MHB.6" "Mitochondrial" "NC.1" "NC.2" "Neurog" "Neuron.1" "Neuron.2"
[22] "r1" "r1&r2" "r2" "r3" "r4" "r5" "r6"
[29] "SC.1" "SC.2" "SC.3" "SC.4"
levels(HB.int) <- c("r1","r1&r2","r2","r3","r4","r5","r6","CaudHB.1","CaudHB.2","CaudHB.3","CaudHB.4","Ciliated","HB","MB.1","MB.2",
"MB.3","MHB.1","MHB.2","MHB.3","MHB.4","MHB.5","MHB.6","Mitochondrial","NC.1","NC.2","Neurog","Neuron.1",
"Neuron.2","SC.1","SC.2","SC.3","SC.4")
3. Heatmaps
3.1 HB13hpf top5 DE genes
All.markers.13 <- FindAllMarkers(HB13hpf, only.pos = T, verbose = F)
top5.pval.13 <- All.markers.13 %>% group_by(cluster) %>% top_n(n=-5, wt = p_val) %>% top_n(n=5, wt = avg_log2FC)
top5.pval.13
Idents(HB13hpf) <- "Clusters"
HB13hpf <- RenameIdents(HB13hpf, "FB.1" = "FB.1 ")
heatmapPlot.13 <- DoHeatmap(HB13hpf, features = unique(top5.pval.13$gene), group.colors = mypal,
size = 5, angle = 45) +
guides(color = FALSE) +
theme(axis.text = element_blank())
heatmapPlot.13

3.2 HB16hpf top5 DE genes
All.markers.16 <- FindAllMarkers(HB16hpf, only.pos = T, verbose = F)
top5.pval.16 <- All.markers.16 %>% group_by(cluster) %>% top_n(n=-5, wt = p_val) %>% top_n(n=5, wt = avg_log2FC)
top5.pval.16
heatmapPlot.16 <- DoHeatmap(HB16hpf, features = unique(top5.pval.16$gene), group.colors = mypal,
size = 5, angle = 45) +
guides(color = FALSE) +
theme(axis.text = element_blank())
heatmapPlot.16

3. Compare r1, r3 & r5 to r2, r4 and r6
r135vs246_markers <- FindMarkers(HB.int,
ident.1 = c("r1","r3","r5"),
ident.2 = c("r2","r4","r6"), verbose = FALSE)
avg_r135vs246 <- AverageExpression(HB.int, features = rownames(r135vs246_markers), assays = "SCT")$SCT
avg_r135vs246 <- as.data.frame(avg_r135vs246) %>%
select(r1, r2, r3, r4, r5, r6)
r135vs246_m_avg <- cbind(r135vs246_markers,avg_r135vs246)
r135over246_m_avg <- r135vs246_m_avg %>%
filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r1 > 0 & r3 > 0 & r5 > 0)
r135over246_m_avg
r246over135_m_avg <- r135vs246_m_avg %>%
filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r2 > 0 & r4 > 0 & r6 > 0)
r246over135_m_avg
3.1 r1/r3/r5 over r2/r4/r6
p135 <- VlnPlot(HB.int, features = rownames(r135over246_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)),
idents = c("r1","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
theme(axis.title.x = element_blank())
p135

kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data",
features = rownames(r135over246_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
kable_classic_2(full_width = F)
| |
r1 |
r1&r2 |
r2 |
r3 |
r4 |
r5 |
r6 |
| egr2b |
0.014 |
0.117 |
0.000 |
3.611 |
0.053 |
4.873 |
0.000 |
| epha4a |
0.348 |
0.640 |
0.687 |
10.365 |
0.773 |
9.833 |
0.222 |
| sema3fb |
0.203 |
0.883 |
0.179 |
6.476 |
0.523 |
6.683 |
0.089 |
| timp2b |
0.087 |
0.252 |
0.119 |
2.802 |
0.030 |
2.937 |
0.067 |
| nab1b |
0.101 |
0.180 |
0.060 |
1.317 |
0.114 |
1.063 |
0.100 |
| myo1cb |
0.058 |
0.009 |
0.000 |
1.048 |
0.030 |
1.262 |
0.011 |
| sema3ab |
0.014 |
0.027 |
0.015 |
0.254 |
0.008 |
1.468 |
0.033 |
| midn |
0.522 |
1.126 |
1.269 |
3.429 |
1.030 |
4.508 |
1.444 |
| gria1a |
0.014 |
0.027 |
0.045 |
0.206 |
0.008 |
1.405 |
0.067 |
| brinp2 |
0.043 |
0.054 |
0.090 |
0.849 |
0.053 |
0.810 |
0.033 |
| tenm3 |
1.246 |
1.144 |
0.657 |
1.270 |
0.742 |
2.603 |
0.978 |
| ackr3b |
0.319 |
0.351 |
0.313 |
0.810 |
0.371 |
2.571 |
0.856 |
| tox3 |
0.710 |
0.946 |
1.403 |
2.397 |
0.598 |
3.325 |
2.000 |
| epha4l |
0.319 |
0.829 |
0.552 |
1.365 |
0.561 |
2.095 |
0.856 |
| ctnnd2b |
1.014 |
0.973 |
0.970 |
1.810 |
1.212 |
2.238 |
0.589 |
| sdk2b |
0.072 |
0.198 |
0.119 |
0.675 |
0.076 |
0.683 |
0.078 |
| celf2 |
1.957 |
1.144 |
2.209 |
1.270 |
1.811 |
4.603 |
0.767 |
3.1 r2/r4/r6 over r1/r3/r5
p246 <- VlnPlot(HB.int, features = rownames(r246over135_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)),
idents = c("r1","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
theme(axis.title.x = element_blank())
p246

kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data",
features = rownames(r246over135_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
kable_classic_2(full_width = F)
| |
r1 |
r1&r2 |
r2 |
r3 |
r4 |
r5 |
r6 |
| col7a1l |
0.029 |
1.216 |
0.806 |
0.246 |
2.242 |
0.246 |
1.611 |
| efnb3b |
3.478 |
3.982 |
4.149 |
0.889 |
3.765 |
1.675 |
5.478 |
| epha4b |
2.304 |
2.883 |
4.284 |
1.857 |
5.379 |
2.889 |
7.322 |
| plxna2 |
0.130 |
0.432 |
1.537 |
0.286 |
0.992 |
0.175 |
0.322 |
| ddr1 |
1.551 |
1.180 |
3.000 |
1.190 |
2.318 |
1.659 |
2.944 |
| qkib |
0.783 |
0.802 |
1.030 |
1.000 |
2.583 |
1.127 |
1.878 |
4. Compare r1 & r2 to rest
r12vsRest_markers <- FindMarkers(HB.int,
ident.1 = c("r1","r1&r2","r2"),
ident.2 = c("r3","r5","r4","r6"), verbose = FALSE)
avg_r12vsRest <- AverageExpression(HB.int, features = rownames(r12vsRest_markers), assays = "SCT")$SCT
avg_r12vsRest <- as.data.frame(avg_r12vsRest) %>%
select(r1, r2, r3, r4, r5, r6)
r12vsRest_m_avg <- cbind(r12vsRest_markers,avg_r12vsRest)
r12overRest_m_avg <- r12vsRest_m_avg %>%
filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r1 > 0 & r2 > 0)
r12overRest_m_avg
# Restoverr12_m_avg <- r12vsRest_m_avg %>%
# filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r3 > 0 & r4 > 0 & r5 > 0 & r6 > 0)
# Restoverr12_m_avg
4.1 r1/r2 over r3/r4/r5/r6
p12 <- VlnPlot(HB.int, features = rownames(r12overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)),
idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
theme(axis.title.x = element_blank())
p12

kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data",
features = rownames(r12overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
kable_classic_2(full_width = F)
| |
r1 |
r1&r2 |
r2 |
r3 |
r4 |
r5 |
r6 |
| efna2a |
8.826 |
4.288 |
2.224 |
1.556 |
1.886 |
0.817 |
0.733 |
| bmpr1ba |
3.000 |
2.369 |
2.657 |
1.198 |
1.576 |
0.778 |
1.089 |
| ek1 |
0.507 |
0.829 |
0.716 |
0.190 |
0.212 |
0.111 |
0.156 |
| nrp2a |
1.072 |
0.982 |
0.149 |
0.024 |
0.167 |
0.048 |
0.078 |
| ptprn2 |
3.246 |
1.468 |
1.821 |
0.698 |
1.091 |
0.548 |
0.822 |
| nr2f1b |
1.609 |
1.288 |
0.806 |
0.206 |
0.705 |
0.063 |
0.256 |
| rasal2 |
5.174 |
4.901 |
4.194 |
2.548 |
3.205 |
2.786 |
3.089 |
| fgfr2 |
6.232 |
5.793 |
3.851 |
2.294 |
3.667 |
2.881 |
4.467 |
| si:ch211-286o17.1 |
2.217 |
1.568 |
1.701 |
0.563 |
0.720 |
0.571 |
1.033 |
| pdzrn3b |
0.957 |
0.730 |
0.373 |
0.103 |
0.212 |
0.127 |
0.133 |
| bahcc1b |
1.333 |
2.414 |
0.985 |
0.984 |
1.447 |
0.421 |
0.556 |
| arhgap29a |
0.696 |
1.153 |
0.955 |
0.484 |
0.455 |
0.262 |
0.178 |
| ror1 |
1.290 |
2.432 |
1.552 |
1.595 |
1.303 |
0.571 |
0.367 |
4.2 r3/r4/r5/r6 over r1/r2
# VlnPlot(HB.int, features = rownames(Restoverr12_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)),
# idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend()
# kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data",
# features = rownames(Restoverr12_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
# dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
# kable_classic_2(full_width = F)
5. Compare r3 & r4 to rest
r34vsRest_markers <- FindMarkers(HB.int,
ident.1 = c("r3","r4"),
ident.2 = c("r1","r1&r2","r2","r5","r6"), verbose = FALSE)
avg_r34vsRest <- AverageExpression(HB.int, features = rownames(r34vsRest_markers), assays = "SCT")$SCT
avg_r34vsRest <- as.data.frame(avg_r34vsRest) %>%
select(r1, r2, r3, r4, r5, r6)
r34vsRest_m_avg <- cbind(r34vsRest_markers,avg_r34vsRest)
r34overRest_m_avg <- r34vsRest_m_avg %>%
filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r3 > 0 & r4 > 0)
r34overRest_m_avg
# Restoverr34_m_avg <- r34vsRest_m_avg %>%
# filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r1 > 0 & r2 > 0 & r5 > 0 & r6 > 0)
# Restoverr34_m_avg
5.1 r3/r4 over r1/r2/r5/r6
p34 <- VlnPlot(HB.int, features = rownames(r34overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)),
idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
theme(axis.title.x = element_blank())
p34

kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data",
features = rownames(r34overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
kable_classic_2(full_width = F)
| |
r1 |
r1&r2 |
r2 |
r3 |
r4 |
r5 |
r6 |
| cyp26b1 |
0.174 |
0.802 |
1.642 |
2.190 |
3.508 |
0.151 |
0.022 |
| cntfr |
7.493 |
6.135 |
12.552 |
11.579 |
13.288 |
8.968 |
6.933 |
| epha4a |
0.348 |
0.640 |
0.687 |
10.365 |
0.773 |
9.833 |
0.222 |
| cadm4 |
0.145 |
0.288 |
0.343 |
0.659 |
1.144 |
0.167 |
0.400 |
| sema3fb |
0.203 |
0.883 |
0.179 |
6.476 |
0.523 |
6.683 |
0.089 |
5.2 r1/r2/r5/r6 over r3/r4
# VlnPlot(HB.int, features = rownames(Restoverr34_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)),
# idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend()
# kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data",
# features = rownames(Restoverr34_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
# dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
# kable_classic_2(full_width = F)
6. Compare r5 & r6 to rest
r56vsRest_markers <- FindMarkers(HB.int,
ident.1 = c("r5","r6"),
ident.2 = c("r1","r1&r2","r2","r3","r4"), verbose = FALSE)
avg_r56vsRest <- AverageExpression(HB.int, features = rownames(r56vsRest_markers), assays = "SCT")$SCT
avg_r56vsRest <- as.data.frame(avg_r56vsRest) %>%
select(r1, r2, r3, r4, r5, r6)
r56vsRest_m_avg <- cbind(r56vsRest_markers,avg_r56vsRest)
r56overRest_m_avg <- r56vsRest_m_avg %>%
filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r5 > 0 & r6 > 0)
r56overRest_m_avg
# Restoverr56_m_avg <- r56vsRest_m_avg %>%
# filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r1 > 0 & r2 > 0 & r3 > 0 & r4 > 0)
# Restoverr56_m_avg
6.1 r5/r6 over r1/r2/r3/r4
p56 <- VlnPlot(HB.int, features = rownames(r56overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)),
idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
theme(axis.title.x = element_blank())
p56

kableExtra::kbl(as.data.frame(
round(AverageExpression(HB.int, assays = "SCT", slot = "data",
features = rownames(r56overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
kable_classic_2(full_width = F)
| |
r1 |
r1&r2 |
r2 |
r3 |
r4 |
r5 |
r6 |
| hoxa4a |
0.000 |
0.072 |
0.015 |
0.000 |
0.015 |
3.778 |
2.367 |
| mafba |
0.029 |
0.144 |
0.000 |
0.016 |
0.114 |
2.865 |
3.356 |
| hoxb3a |
0.043 |
0.135 |
0.000 |
0.508 |
0.652 |
3.381 |
2.789 |
| evx1 |
0.014 |
0.036 |
0.000 |
0.016 |
0.061 |
0.825 |
0.456 |
| si:ch211-216b21.2 |
0.058 |
0.243 |
0.045 |
0.111 |
0.015 |
1.238 |
0.878 |
| tenm4 |
3.899 |
5.486 |
9.507 |
3.746 |
1.955 |
8.389 |
12.033 |
| gria1a |
0.014 |
0.027 |
0.045 |
0.206 |
0.008 |
1.405 |
0.067 |
| col11a1b |
0.072 |
0.081 |
0.119 |
0.095 |
0.114 |
0.802 |
1.489 |
| hoxd3a |
0.029 |
0.090 |
0.000 |
0.008 |
0.015 |
0.087 |
1.122 |
| ptprua |
0.101 |
0.135 |
0.179 |
0.151 |
0.106 |
2.444 |
0.411 |
| sema3ab |
0.014 |
0.027 |
0.015 |
0.254 |
0.008 |
1.468 |
0.033 |
| cyp26c1 |
0.696 |
1.018 |
1.388 |
0.302 |
0.818 |
1.802 |
5.467 |
| ackr3b |
0.319 |
0.351 |
0.313 |
0.810 |
0.371 |
2.571 |
0.856 |
| dclk2a |
0.275 |
0.270 |
0.179 |
0.548 |
0.258 |
1.175 |
1.467 |
| antxr1c |
0.101 |
0.135 |
0.075 |
0.071 |
0.114 |
0.508 |
1.000 |
| tshz3b |
0.029 |
0.045 |
0.045 |
0.063 |
0.492 |
0.929 |
0.544 |
| zgc:158328 |
0.029 |
0.117 |
0.194 |
0.087 |
0.500 |
0.738 |
1.633 |
| tiam1a |
0.594 |
1.036 |
1.119 |
0.690 |
0.848 |
2.413 |
1.400 |
| cxcl12a |
0.319 |
0.333 |
1.507 |
0.333 |
0.970 |
1.563 |
1.900 |
| nhsl1a |
0.043 |
0.216 |
0.134 |
0.111 |
0.061 |
0.460 |
0.956 |
| iqca1 |
0.101 |
0.081 |
0.030 |
0.262 |
0.045 |
0.881 |
0.289 |
| inavaa |
0.014 |
0.477 |
0.164 |
0.635 |
0.485 |
2.143 |
1.089 |
| tox3 |
0.710 |
0.946 |
1.403 |
2.397 |
0.598 |
3.325 |
2.000 |
| cbfa2t2 |
0.464 |
0.135 |
0.194 |
0.452 |
0.227 |
1.135 |
0.700 |
| rarab |
0.696 |
0.811 |
0.567 |
0.825 |
1.114 |
1.675 |
1.878 |
| nck2a |
1.000 |
1.892 |
1.955 |
2.238 |
1.250 |
3.976 |
2.944 |
| nos1apa.1 |
1.855 |
1.333 |
1.493 |
1.746 |
1.485 |
2.659 |
3.700 |
| dennd5b |
0.841 |
1.000 |
1.209 |
0.524 |
0.735 |
1.849 |
2.233 |
| magi2a |
1.029 |
0.757 |
1.343 |
0.849 |
0.909 |
3.262 |
2.000 |
| map7d1a |
2.014 |
1.568 |
1.299 |
0.746 |
1.447 |
2.214 |
3.067 |
| adgrl1a |
0.420 |
1.333 |
0.507 |
0.857 |
2.394 |
2.881 |
3.378 |
| rnf165a |
0.710 |
1.216 |
0.627 |
0.984 |
1.106 |
2.302 |
1.911 |
| vat1 |
0.174 |
0.324 |
0.642 |
0.341 |
0.409 |
1.063 |
0.911 |
| col15a1b |
0.145 |
0.667 |
0.254 |
0.254 |
0.492 |
1.802 |
0.822 |
| chl1a |
0.261 |
0.297 |
0.090 |
0.111 |
0.076 |
0.310 |
1.378 |
| midn |
0.522 |
1.126 |
1.269 |
3.429 |
1.030 |
4.508 |
1.444 |
| dbn1 |
0.522 |
0.865 |
0.582 |
1.254 |
1.258 |
2.159 |
2.422 |
| dtnba |
0.696 |
0.559 |
1.940 |
1.333 |
1.803 |
2.349 |
2.400 |
| timp2b |
0.087 |
0.252 |
0.119 |
2.802 |
0.030 |
2.937 |
0.067 |
| adgrl3.1 |
0.696 |
0.730 |
0.642 |
0.849 |
0.894 |
1.786 |
1.211 |
| epha4l |
0.319 |
0.829 |
0.552 |
1.365 |
0.561 |
2.095 |
0.856 |
| ctnna2 |
0.275 |
0.324 |
0.552 |
0.349 |
0.917 |
1.119 |
1.644 |
| col12a1a |
0.391 |
0.477 |
0.448 |
0.381 |
0.348 |
0.810 |
1.911 |
| sema3fb |
0.203 |
0.883 |
0.179 |
6.476 |
0.523 |
6.683 |
0.089 |
| epha4a |
0.348 |
0.640 |
0.687 |
10.365 |
0.773 |
9.833 |
0.222 |
| tenm3 |
1.246 |
1.144 |
0.657 |
1.270 |
0.742 |
2.603 |
0.978 |
| jag1a |
0.130 |
0.036 |
0.045 |
0.151 |
0.045 |
0.857 |
0.333 |
6.2 r1/r2/r3/r4 over r5/r6
# VlnPlot(HB.int, features = rownames(Restoverr56_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)),
# idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend()
# kableExtra::kbl(as.data.frame(
# round(AverageExpression(HB.int, assays = "SCT", slot = "data",
# features = rownames(Restoverr56_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
# dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
# kable_classic_2(full_width = F)
write.table(r135vs246_m_avg, file = "../results/rhom_DEgenes_135vs246.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r12vsRest_m_avg, file = "../results/rhom_DEgenes_12vs3456.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r34vsRest_m_avg, file = "../results/rhom_DEgenes_34vs1256.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r56vsRest_m_avg, file = "../results/rhom_DEgenes_56vs1234.txt", sep = "\t", quote = FALSE, col.names = NA)
combined <-
(((heatmapPlot.13) +
#plot_spacer() +
(heatmapPlot.16) #+
#plot_layout(widths = c(2,0.1,2))
) /
((plot_spacer()) +
((p135 / plot_spacer() / p246 / plot_spacer() / p12 / plot_spacer() / p34) +
plot_layout(heights = c(17,0.1,6,0.1,13,0.1,5))) +
#plot_spacer() +
(p56) +
plot_layout(widths = c(3,1,1))
)) +
plot_layout(heights = c(1,1.5))
combined

ggsave(filename = "../results/Fig5_combinedPlot.png", plot = combined)
Saving 31.2 x 31.2 in image
sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 12.3
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods base
other attached packages:
[1] harmony_0.1.0 Rcpp_1.0.7 AnnotationHub_3.2.0
[4] BiocFileCache_2.2.0 dbplyr_2.1.1 motifmatchr_1.16.0
[7] TFBSTools_1.32.0 patchwork_1.1.2 JASPAR2020_0.99.10
[10] limma_3.50.3 ggsci_2.9 ggplot2_3.4.0
[13] dplyr_1.0.7 kableExtra_1.3.4 knitr_1.36
[16] Signac_1.2.1 BSgenome.Drerio.UCSC.danRer11_1.4.2 BSgenome_1.62.0
[19] rtracklayer_1.54.0 Biostrings_2.62.0 XVector_0.34.0
[22] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0 org.Dr.eg.db_3.14.0
[25] AnnotationDbi_1.56.1 IRanges_2.28.0 S4Vectors_0.32.4
[28] Biobase_2.54.0 BiocGenerics_0.40.0 SeuratObject_4.0.4
[31] Seurat_4.0.1
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 SnowballC_0.7.0 scattermore_0.7
[4] R.methodsS3_1.8.1 tidyr_1.1.4 bit64_4.0.5
[7] irlba_2.3.3 DelayedArray_0.20.0 R.utils_2.11.0
[10] data.table_1.14.2 rpart_4.1-15 KEGGREST_1.34.0
[13] RCurl_1.98-1.5 generics_0.1.1 cowplot_1.1.1
[16] RSQLite_2.2.8 RANN_2.6.1 future_1.26.1
[19] bit_4.0.4 tzdb_0.2.0 spatstat.data_2.1-0
[22] webshot_0.5.4 xml2_1.3.3 httpuv_1.6.3
[25] SummarizedExperiment_1.24.0 assertthat_0.2.1 DirichletMultinomial_1.36.0
[28] xfun_0.27 hms_1.1.1 jquerylib_0.1.4
[31] evaluate_0.14 promises_1.2.0.1 fansi_0.5.0
[34] restfulr_0.0.13 caTools_1.18.2 igraph_1.2.8
[37] DBI_1.1.1 htmlwidgets_1.5.4 sparsesvd_0.2
[40] spatstat.geom_2.3-0 purrr_0.3.4 ellipsis_0.3.2
[43] annotate_1.72.0 deldir_1.0-6 MatrixGenerics_1.6.0
[46] vctrs_0.5.0 ROCR_1.0-11 abind_1.4-5
[49] cachem_1.0.6 withr_2.5.0 ggforce_0.3.3
[52] sctransform_0.3.3 GenomicAlignments_1.30.0 goftest_1.2-3
[55] svglite_2.1.0 cluster_2.1.2 ape_5.6-2
[58] lazyeval_0.2.2 seqLogo_1.60.0 crayon_1.4.2
[61] labeling_0.4.2 pkgconfig_2.0.3 slam_0.1-48
[64] tweenr_1.0.2 nlme_3.1-153 rlang_1.0.6
[67] globals_0.15.1 lifecycle_1.0.3 miniUI_0.1.1.1
[70] filelock_1.0.2 polyclip_1.10-0 matrixStats_0.61.0
[73] lmtest_0.9-38 Matrix_1.3-4 ggseqlogo_0.1
[76] zoo_1.8-9 ggridges_0.5.3 png_0.1-7
[79] viridisLite_0.4.0 rjson_0.2.20 bitops_1.0-7
[82] R.oo_1.24.0 KernSmooth_2.23-20 blob_1.2.2
[85] stringr_1.4.0 parallelly_1.32.0 readr_2.0.2
[88] CNEr_1.30.0 scales_1.2.1 memoise_2.0.0
[91] magrittr_2.0.1 plyr_1.8.6 ica_1.0-2
[94] zlibbioc_1.40.0 compiler_4.1.0 BiocIO_1.4.0
[97] RColorBrewer_1.1-2 fitdistrplus_1.1-6 Rsamtools_2.10.0
[100] cli_3.4.1 listenv_0.8.0 pbapply_1.5-0
[103] MASS_7.3-54 mgcv_1.8-38 tidyselect_1.1.1
[106] stringi_1.7.5 highr_0.9 yaml_2.2.1
[109] ggrepel_0.9.1 grid_4.1.0 sass_0.4.0
[112] fastmatch_1.1-3 tools_4.1.0 future.apply_1.8.1
[115] parallel_4.1.0 rstudioapi_0.13 TFMPvalue_0.0.8
[118] lsa_0.73.2 gridExtra_2.3 farver_2.1.0
[121] Rtsne_0.15 digest_0.6.28 BiocManager_1.30.19
[124] shiny_1.7.1 pracma_2.3.3 qlcMatrix_0.9.7
[127] BiocVersion_3.14.0 later_1.3.0 RcppAnnoy_0.0.19
[130] httr_1.4.2 colorspace_2.0-2 rvest_1.0.3
[133] XML_3.99-0.8 tensor_1.5 reticulate_1.22
[136] splines_4.1.0 uwot_0.1.10 RcppRoll_0.3.0
[139] spatstat.utils_2.2-0 renv_0.15.5 plotly_4.10.0
[142] systemfonts_1.0.4 xtable_1.8-4 jsonlite_1.7.2
[145] poweRlaw_0.70.6 R6_2.5.1 pillar_1.6.4
[148] htmltools_0.5.2 mime_0.12 glue_1.6.2
[151] fastmap_1.1.0 BiocParallel_1.28.0 interactiveDisplayBase_1.32.0
[154] codetools_0.2-18 utf8_1.2.2 lattice_0.20-45
[157] bslib_0.3.1 spatstat.sparse_2.0-0 tibble_3.1.6
[160] curl_4.3.2 leiden_0.3.9 gtools_3.9.2
[163] GO.db_3.14.0 survival_3.2-13 rmarkdown_2.11
[166] docopt_0.7.1 munsell_0.5.0 GenomeInfoDbData_1.2.7
[169] reshape2_1.4.4 gtable_0.3.0 spatstat.core_2.3-0
---
title: "Figure 5 R Notebook"
output: html_notebook
---

```{r libraries, results=F}
suppressPackageStartupMessages({
  library(Seurat)
  library(org.Dr.eg.db)
  library(BSgenome.Drerio.UCSC.danRer11)
  library(Signac)
  library(knitr)
  library(kableExtra)
  library(dplyr)
  library(ggplot2)
  library(ggsci)
  library(limma)
  library(JASPAR2020)
  library(patchwork)
  library(TFBSTools)
  library(motifmatchr)
  library(AnnotationHub)
  library(harmony)
})
options(future.globals.maxSize = 4000 * 1024^2)
```

```{r mypal}
mypal <- pal_igv(palette = "default",alpha = 1)(30)
```

# 2. Read data

```{r}
HB13hpf <- readRDS(file = "RDSfiles/HB13hpf_neural.RDS")
DefaultAssay(HB13hpf) <- "SCT"
Idents(HB13hpf) <- "Clusters"
HB16hpf <- readRDS(file = "RDSfiles/HB16hpf_neural.RDS")
DefaultAssay(HB16hpf) <- "SCT"
Idents(HB16hpf) <- "Clusters"
```

```{r}
HB.int <- readRDS(file = "RDSfiles/int.neural.3WT.subset.RDS")
DefaultAssay(HB.int) <- "SCT"
DimPlot(HB.int, reduction = "wnn.umap") + scale_color_igv()
```

```{r}
HB.int$intClusters <- as.character(HB.int$intClusters)
HB.int$intClusters[HB.int$intClusters %in% c("r1&r2.1","r1&r2.2")] <- "r1&r2"
HB.int$intClusters[HB.int$intClusters %in% c("r3.1")] <- "r3"
HB.int$intClusters[HB.int$intClusters %in% c("r4.1","r4.2")] <- "r4"
HB.int$intClusters[HB.int$intClusters %in% c("r5.1","r5.2")] <- "r5"
HB.int$intClusters[HB.int$intClusters %in% c("r6.1","r6.2")] <- "r6"
HB.int$intClusters <- droplevels(as.factor(HB.int$intClusters))
Idents(HB.int) <- "intClusters"
levels(HB.int)
```

```{r}
levels(HB.int) <- c("r1","r1&r2","r2","r3","r4","r5","r6","CaudHB.1","CaudHB.2","CaudHB.3","CaudHB.4","Ciliated","HB","MB.1","MB.2",
                    "MB.3","MHB.1","MHB.2","MHB.3","MHB.4","MHB.5","MHB.6","Mitochondrial","NC.1","NC.2","Neurog","Neuron.1", 
                    "Neuron.2","SC.1","SC.2","SC.3","SC.4")
```

# 3. Heatmaps

## 3.1 HB13hpf top5 DE genes
```{r markers.13, results=F}
All.markers.13 <- FindAllMarkers(HB13hpf, only.pos = T, verbose = F)
```

```{r top5.13}
top5.pval.13 <- All.markers.13 %>% group_by(cluster) %>% top_n(n=-5, wt = p_val) %>% top_n(n=5, wt = avg_log2FC)
top5.pval.13
```

```{r heatmap.13, fig.height=7, fig.width=7}
Idents(HB13hpf) <- "Clusters"
HB13hpf <- RenameIdents(HB13hpf, "FB.1" = "FB.1       ") ## this makes the length of longest name same as HB16hpf so they even in combined plot
heatmapPlot.13 <- DoHeatmap(HB13hpf, features = unique(top5.pval.13$gene), group.colors = mypal, 
                         size = 5, angle = 45) + 
  guides(color = FALSE) +
  theme(axis.text = element_blank())
heatmapPlot.13
```

## 3.2 HB16hpf top5 DE genes
```{r markers.16, results=F}
All.markers.16 <- FindAllMarkers(HB16hpf, only.pos = T, verbose = F)
```

```{r top5.16}
top5.pval.16 <- All.markers.16 %>% group_by(cluster) %>% top_n(n=-5, wt = p_val) %>% top_n(n=5, wt = avg_log2FC)
top5.pval.16
```

```{r heatmap.16, fig.height=7, fig.width=7}
heatmapPlot.16 <- DoHeatmap(HB16hpf, features = unique(top5.pval.16$gene), group.colors = mypal, 
                         size = 5, angle = 45) + 
  guides(color = FALSE) +
  theme(axis.text = element_blank())
heatmapPlot.16
```

# 3. Compare r1, r3 & r5 to r2, r4 and r6
```{r markers135}
r135vs246_markers <- FindMarkers(HB.int, 
                                 ident.1 = c("r1","r3","r5"), 
                                 ident.2 = c("r2","r4","r6"), verbose = FALSE)
```

```{r avg135}
avg_r135vs246 <- AverageExpression(HB.int, features = rownames(r135vs246_markers), assays = "SCT")$SCT
avg_r135vs246 <- as.data.frame(avg_r135vs246) %>%
  select(r1, r2, r3, r4, r5, r6)
r135vs246_m_avg <- cbind(r135vs246_markers,avg_r135vs246)
```


```{r table135}
r135over246_m_avg <- r135vs246_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r1 > 0 & r3 > 0 & r5 > 0)
r135over246_m_avg
```

```{r table246}
r246over135_m_avg <- r135vs246_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r2 > 0 & r4 > 0 & r6 > 0)
r246over135_m_avg
```

## 3.1 r1/r3/r5 over r2/r4/r6
```{r vln135, fig.width=6, fig.height=5}
p135 <- VlnPlot(HB.int, features = rownames(r135over246_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p135
```

```{r kable135}
kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r135over246_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)
```

## 3.1 r2/r4/r6 over r1/r3/r5
```{r vln246, fig.width=6, fig.height=5}
p246 <- VlnPlot(HB.int, features = rownames(r246over135_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p246
```

```{r kable246}
kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r246over135_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)
```


# 4. Compare r1 & r2 to rest
```{r markers12}
r12vsRest_markers <- FindMarkers(HB.int, 
                                 ident.1 = c("r1","r1&r2","r2"), 
                                 ident.2 = c("r3","r5","r4","r6"), verbose = FALSE)
```

```{r avg12}
avg_r12vsRest <- AverageExpression(HB.int, features = rownames(r12vsRest_markers), assays = "SCT")$SCT
avg_r12vsRest <- as.data.frame(avg_r12vsRest) %>%
  select(r1, r2, r3, r4, r5, r6)
r12vsRest_m_avg <- cbind(r12vsRest_markers,avg_r12vsRest)
```


```{r table12}
r12overRest_m_avg <- r12vsRest_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r1 > 0 & r2 > 0)
r12overRest_m_avg
```

```{r table3456}
# Restoverr12_m_avg <- r12vsRest_m_avg %>%
#   filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r3 > 0 & r4 > 0 & r5 > 0 & r6 > 0)
# Restoverr12_m_avg
```

## 4.1 r1/r2 over r3/r4/r5/r6
```{r vln12, fig.width=4, fig.height=8}
p12 <- VlnPlot(HB.int, features = rownames(r12overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p12
```

```{r kable12}
kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r12overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)
```

## 4.2 r3/r4/r5/r6 over r1/r2
```{r vln3456, fig.width=4, fig.height=8}
# VlnPlot(HB.int, features = rownames(Restoverr12_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
#         idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend()
```

```{r kable3456}
# kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
#                         features = rownames(Restoverr12_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
#   dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
#   kable_classic_2(full_width = F)
```

# 5. Compare r3 & r4 to rest
```{r markers34}
r34vsRest_markers <- FindMarkers(HB.int, 
                                 ident.1 = c("r3","r4"), 
                                 ident.2 = c("r1","r1&r2","r2","r5","r6"), verbose = FALSE)
```

```{r avg34}
avg_r34vsRest <- AverageExpression(HB.int, features = rownames(r34vsRest_markers), assays = "SCT")$SCT
avg_r34vsRest <- as.data.frame(avg_r34vsRest) %>%
  select(r1, r2, r3, r4, r5, r6)
r34vsRest_m_avg <- cbind(r34vsRest_markers,avg_r34vsRest)
```

```{r table34}
r34overRest_m_avg <- r34vsRest_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r3 > 0 & r4 > 0)
r34overRest_m_avg
```

```{r table1256}
# Restoverr34_m_avg <- r34vsRest_m_avg %>%
#   filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r1 > 0 & r2 > 0 & r5 > 0 & r6 > 0)
# Restoverr34_m_avg
```
## 5.1 r3/r4 over r1/r2/r5/r6
```{r vln34, fig.width=4, fig.height=5}
p34 <- VlnPlot(HB.int, features = rownames(r34overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p34
```

```{r kable34}
kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r34overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)
```

## 5.2 r1/r2/r5/r6 over r3/r4
```{r vln1256, fig.width=4, fig.height=5}
# VlnPlot(HB.int, features = rownames(Restoverr34_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
#         idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend()
```

```{r kable1256}
# kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
#                         features = rownames(Restoverr34_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
#   dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
#   kable_classic_2(full_width = F)
```

# 6. Compare r5 & r6 to rest
```{r markers56}
r56vsRest_markers <- FindMarkers(HB.int, 
                                 ident.1 = c("r5","r6"), 
                                 ident.2 = c("r1","r1&r2","r2","r3","r4"), verbose = FALSE)
```

```{r avg56}
avg_r56vsRest <- AverageExpression(HB.int, features = rownames(r56vsRest_markers), assays = "SCT")$SCT
avg_r56vsRest <- as.data.frame(avg_r56vsRest) %>%
  select(r1, r2, r3, r4, r5, r6)
r56vsRest_m_avg <- cbind(r56vsRest_markers,avg_r56vsRest)
```

```{r table56}
r56overRest_m_avg <- r56vsRest_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r5 > 0 & r6 > 0)
r56overRest_m_avg
```

```{r table1234}
# Restoverr56_m_avg <- r56vsRest_m_avg %>%
#   filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r1 > 0 & r2 > 0 & r3 > 0 & r4 > 0)
# Restoverr56_m_avg
```
## 6.1 r5/r6 over r1/r2/r3/r4
```{r vln56, fig.width=4, fig.height=10}
p56 <- VlnPlot(HB.int, features = rownames(r56overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p56
```

```{r kable56}
kableExtra::kbl(as.data.frame(
  round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r56overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)
```

## 6.2 r1/r2/r3/r4 over r5/r6
```{r vln1234, fig.width=4, fig.height=10}
# VlnPlot(HB.int, features = rownames(Restoverr56_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
#         idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() 
```

```{r kable1234}
# kableExtra::kbl(as.data.frame(
#   round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
#                         features = rownames(Restoverr56_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
#   dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
#   kable_classic_2(full_width = F)
```

```{r write}
write.table(r135vs246_m_avg, file = "../results/rhom_DEgenes_135vs246.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r12vsRest_m_avg, file = "../results/rhom_DEgenes_12vs3456.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r34vsRest_m_avg, file = "../results/rhom_DEgenes_34vs1256.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r56vsRest_m_avg, file = "../results/rhom_DEgenes_56vs1234.txt", sep = "\t", quote = FALSE, col.names = NA)
```

```{r combined2, fig.height=25, fig.width=25}
combined <- 
  (((heatmapPlot.13) +
      #plot_spacer() +
     (heatmapPlot.16) #+ 
     #plot_layout(widths = c(2,0.1,2))
  ) /
  ((plot_spacer()) +
     ((p135 / plot_spacer() / p246 / plot_spacer() / p12 / plot_spacer() / p34) + 
        plot_layout(heights = c(17,0.1,6,0.1,13,0.1,5))) + 
     #plot_spacer() +
     (p56) +
     plot_layout(widths = c(3,1,1))
  )) + 
  plot_layout(heights = c(1,1.5))
combined
ggsave(filename = "../results/Fig5_combinedPlot.png", plot = combined)
```
```{r sessioninfo}
sessionInfo()
```

